Perceived risk of Type 2 diabetes in Australian women with a recent history of gestational diabetes mellitus
Bibliographic record
Abstract
AIMS: To describe the risk perceptions and factors associated with a high level of perceived risk for the development of Type 2 diabetes in a sample of Australian women with a recent history of gestational diabetes mellitus. METHODS: Participants were women aged 18 years and over, diagnosed with gestational diabetes between 2003 and 2005, registered with the National Diabetes Services Scheme. Cross-sectional data were collected via written postal survey and included a self-reported risk perception scale. RESULTS: Of 4098 invited, eligible women, 1372 consented to participate (response rate 36%). Respondents currently pregnant or subsequently diagnosed with Type 2 diabetes were excluded (n = 196). Up to 3 years post-gestational diabetes, 32% of women perceived that they were at a low or very low risk for developing Type 2 diabetes, 42% at moderate risk and 26% high or very high risk. Using logistic regression analysis, factors associated with high level of perceived risk were body mass index > 25 kg/m(2) [odds ratio (OR) 4.50, 95% confidence interval (CI) (3.12, 6.51)], a family history of diabetes [OR 3.80, 95% CI (2.67, 5.33)] and use of insulin during pregnancy [OR 1.92, 95% CI (1.31, 2.61)]. CONCLUSIONS: Although women with known risk factors for Type 2 diabetes were more likely to perceive their risk as high, we found that one third still considered themselves to be at low or very low risk for the development of diabetes. These results suggest a need for increased awareness of gestational diabetes as a strong predictor of Type 2 diabetes risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".